Algae are key contributors to global carbon fixation and form the basis of many food webs. In nature, their growth is often supported or suppressed by microorganisms. The bacterium Pseudomonas protegens Pf-5 arrests the growth of the green unicellular alga Chlamydomonas reinhardtii, deflagellates the alga by the cyclic lipopeptide orfamide A, and alters its morphology [P. Aiyar et al., Nat. Commun. 8, 1756 (2017)]. Using a combination of Raman microspectroscopy, genome mining, and mutational analysis, we discovered a polyyne toxin, protegencin, which is secreted by P. protegens, penetrates the algal cells, and causes destruction of the carotenoids of their primitive visual system, the eyespot. Together with secreted orfamide A, protegencin thus prevents the phototactic behavior of C. reinhardtii. A mutant of P. protegens deficient in protegencin production does not affect growth or eyespot carotenoids of C. reinhardtii. Protegencin acts in a direct and destructive way by lysing and killing the algal cells. The toxic effect of protegencin is also observed in an eyeless mutant and with the colony-forming Chlorophyte alga Gonium pectorale. These data reveal a two-pronged molecular strategy involving a cyclic lipopeptide and a conjugated tetrayne used by bacteria to attack select Chlamydomonad algae. In conjunction with the bloom-forming activity of several chlorophytes and the presence of the protegencin gene cluster in over 50 different Pseudomonas genomes [A. J. Mullins et al., bioRxiv [Preprint] (2021). https://www.biorxiv.org/content/10.1101/2021.03.05.433886v1 (Accessed 17 April 2021)], these data are highly relevant to ecological interactions between Chlorophyte algae and Pseudomonadales bacteria. Significance Algae live in association with microbes that interact by a variety of chemical mediators, resulting in mutualistic or antagonistic relationships. Although algae are key contributors to carbon fixation and are fundamental for food webs, we still know little about the underlying molecular mechanisms affecting their fitness. This study investigates the interaction between an antagonistic bacterium and a unicellular alga. It demonstrates multiple roles of a polyyne, protegencin, that is used by the bacteria to attack green algal cells. It is a highly effective toxin that alters a subcellular algal compartment used for vision, bleaches, and lyses the algal cells. These results expand our knowledge of the arsenal of chemical mediators in bacteria and their modes of action in algal communities.

Fluorescence lifetime imaging microscopy (FLIM) has emerged as a promising tool for all scientific studies in recent years. However, the utilization of FLIM data requires complex data modeling techniques, such as curve-fitting procedures. These conventional curve-fitting procedures are not only computationally intensive but also time-consuming. To address this limitation, machine learning (ML), particularly deep learning (DL), can be employed. This review aims to focus on the ML and DL methods for FLIM data analysis. Subsequently, ML and DL strategies for evaluating FLIM data are discussed, consisting of preprocessing, data modeling, and inverse modeling. Additionally, the advantages of the reviewed methods are deliberated alongside future implications. Furthermore, several freely available software packages for analyzing the FLIM data are highlighted.

The self-healing behavior of two supramolecular polymers based on π–π-interactions featuring different polymer backbones is presented. For this purpose, these polymers were synthesized utilizing a polycondensation of a perylene tetracarboxylic dianhydride with polyether-based diamines and the resulting materials were investigated using various analytical techniques. Thus, the molecular structure of the polymers could be correlated with the ability for self-healing. Moreover, the mechanical behavior was studied using rheology. The activation of the supramolecular interactions results in a breaking of these noncovalent bonds, which was investigated using IR spectroscopy, leading to a sufficient increase in mobility and, finally, a healing of the mechanical damage. This scratch-healing behavior was also quantified in detail using an indenter.

This work presents the synthesis and characterization of easily tuneable shape-memory metallopolymers. Furthermore, the structural design enables excellent rewriting properties. For this purpose, two different polymers were synthesized using either 2-ethyl hexyl- or butyl methacrylate monomers featuring both histidine and terpyridine ligands in their side chains. The addition of metal salt resulted in the selective formation of stable complexes with the terpyridine ligands (NiCl₂ or FeSO₄) and addition of a second metal ion led to labile ones with the histidine ligands (Zn(TFMS)₂ or NiCl₂). As a consequence, a dual supramolecular crosslinked metallopolymer network was formed, which was confirmed using Raman spectroscopy. Due to the use of different metal salts, it is possible to tune the stability of both complexes independently and, thus, the switching and rewriting temperature of the shape-memory polymer. Fixity and recovery rates up to 99% were obtained by this approach. Furthermore, thermo mechanical analyses revealed the rewriting possibility without reducing the shape-memory abilities.

This paper introduces the newly developed principal component powered two-dimensional (2D) correlation spectroscopy (PC 2D-COS) as an alternative approach to 2D correlation spectroscopy taking advantage of a dimensionality reduction by principal component analysis. It is shown that PC 2D-COS is equivalent to traditional 2D correlation analysis while providing a significant advantage in terms of computational complexity and memory consumption. These features allow for an easy calculation of 2D correlation spectra even for data sets with very high spectral resolution or a parallel analysis of multiple data sets of 2D correlation spectra. Along with this reduction in complexity, PC 2D-COS offers a significant noise rejection property by limiting the set of principal components used for the 2D correlation calculation. As an example for the application of truncated PC 2D-COS a temperature-dependent Raman spectroscopic data set of a fullerene-anthracene adduct is examined. It is demonstrated that a large reduction in computational cost is possible without loss of relevant information, even for complex real world data sets.

This study presents the simplified synthetic procedure of rewritable shape-memory metallopolymers, additionally featuring self-healing abilities. The formation of different orthogonal metal-complexes is applied to prepare interpenetrating metallopolymer networks (IPNs). Two different linear polymers, one bearing terpyridine and the other containing trt-histidine ligands in the side chains, are individually synthesized via a controlled radical polymerization procedure. After the simple mixing of both polymers, metal salt(s) are added inducing the formation of stable terpyridine and labile trt-histidine metal complexes simultaneously, whereby an interpenetrating metallopolymer structure is generated. FT-Raman spectroscopy is applied to confirm the successful formation of the desired complexes. The resulting IPNs are investigated in a detailed fashion via differential scanning calorimetry as well as thermogravimetric analyses. Furthermore, thermo-mechanical analysis revealed excellent shape-memory abilities with strain fixity and recovery rates up to 99%. Moreover, as part of this kind of investigation, the rewriting of the permanent shape and its influence on the shape-memory abilities is studied, revealing the possibility to program and determine a completely new permanent shape without reducing the shape-memory abilities in a significant manner. Additionally, self-healing abilities are observed and scratch-healing efficiencies up to 98% could be reached.